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groups are strongly encouraged to apply. Your Tasks: Development and application of algorithms for modelling, evaluation and visualization of ultrafast processes Investigation of ultrafast dynamics in
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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algorithms to compute similarity between interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders
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: Multibody system dynamics Grant/funding period: START: 01/09/2023 END: 31/08/2026 Centre of Principal Researcher: EPEF PURPOSE OF CONTRACT: Development and experimental validation of data fusion-based methods
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: Multibody system dynamics Grant/funding period: START: 01/09/2023 END: 31/08/2026 Centre of Principal Researcher: EPEF PURPOSE OF CONTRACT: Development and experimental validation of data fusion-based methods
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developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize
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interference, while ensuring energy-efficient and scalable operation. This PhD project will focus on developing machine learning algorithms to enable robust channel estimation, intelligent user association
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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. The goal is to develop a language-agnostic verification approach, following on promising results related to the Solidity language. - While our current digital infrastructure relies on classical networks